Multi-output prediction of dose-response curves enables drug repositioning and biomarker discovery.

ABSTRACT: Drug response prediction is hampered by uncertainty in the measures of response and selection of doses. In this study, we propose a probabilistic multi-output model to simultaneously predict all dose-responses and uncover their biomarkers. By describing the relationship between genomic features and chemical properties to every response at every dose, our multi-output Gaussian Process (MOGP) models enable assessment of drug efficacy using any dose-response metric. This approach was tested across two drug screening studies and ten cancer types. Kullback-leibler divergence measured the importance of each feature and identified EZH2 gene as a novel biomarker of BRAF inhibitor response. We demonstrate the effectiveness of our MOGP models in accurately predicting dose-responses in different canc

SUBMITTER: Gutierrez JG 

PROVIDER: S-EPMC11415488 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature